Visualizing structure and transitions in high-dimensional biological data.,Nature Biotechnology

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Visualizing structure and transitions in high-dimensional biological data.
Nature Biotechnology ( IF 33.1 ) Pub Date : 2019-12-03 , DOI: 10.1038/s41587-019-0336-3
Kevin R Moon ₁ , David van Dijk _{2,

3} , Zheng Wang _{4,

5} , Scott Gigante ₆ , Daniel B Burkhardt ₇ , William S Chen ₇ , Kristina Yim ₇ , Antonia van den Elzen ₇ , Matthew J Hirn _{8,

9} , Ronald R Coifman ₁₀ , Natalia B Ivanova ₁₁ , Guy Wolf _{12,

13} , Smita Krishnaswamy _{3,

7}

Affiliation

Department of Mathematics and Statistics, Utah State University, Logan, UT, USA.
Cardiovascular Research Center, section Cardiology, Department of Internal Medicine, Yale University, New Haven, CT, USA.
Department of Computer Science, Yale University, New Haven, CT, USA.
School of Basic Medicine, Qingdao University, Qingdao, China.
Yale Stem Cell Center, Department of Genetics, Yale University, New Haven, CT, USA.
Computational Biology and Bioinformatics Program, Yale University, New Haven, CT, USA.
Department of Genetics, Yale University, New Haven, CT, USA.
Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI, USA.
Department of Mathematics, Michigan State University, East Lansing, MI, USA.
Applied Mathematics Program, Yale University, New Haven, CT, USA.
Department of Genetics, Center for Molecular Medicine, University of Georgia, Athens, GA, USA.
Department of Mathematics and Statistics, Université de Montréal, Montréal, Quebec, Canada.
Mila-Quebec Artificial Intelligence Institute, Montréal, Quebec, Canada.

The high-dimensional data created by high-throughput technologies require visualization tools that reveal data structure and patterns in an intuitive form. We present PHATE, a visualization method that captures both local and global nonlinear structure using an information-geometric distance between data points. We compare PHATE to other tools on a variety of artificial and biological datasets, and find that it consistently preserves a range of patterns in data, including continual progressions, branches and clusters, better than other tools. We define a manifold preservation metric, which we call denoised embedding manifold preservation (DEMaP), and show that PHATE produces lower-dimensional embeddings that are quantitatively better denoised as compared to existing visualization methods. An analysis of a newly generated single-cell RNA sequencing dataset on human germ-layer differentiation demonstrates how PHATE reveals unique biological insight into the main developmental branches, including identification of three previously undescribed subpopulations. We also show that PHATE is applicable to a wide variety of data types, including mass cytometry, single-cell RNA sequencing, Hi-C and gut microbiome data.

中文翻译：

可视化高维生物数据的结构和转换。

高通量技术创建的高维数据需要可视化工具以直观的形式揭示数据结构和模式。我们提出了 PHATE，一种可视化方法，它使用数据点之间的信息几何距离来捕获局部和全局非线性结构。我们在各种人工和生物数据集上将 PHATE 与其他工具进行比较，发现它比其他工具更好地始终保留了数据中的一系列模式，包括连续级数、分支和集群。我们定义了一个流形保留度量，我们称之为去噪嵌入流形保留（DEMaP），并表明 PHATE 产生的低维嵌入在数量上比现有的可视化方法更好地去噪。对新生成的有关人类胚层分化的单细胞 RNA 测序数据集的分析表明，PHATE 如何揭示对主要发育分支的独特生物学见解，包括识别三个先前未描述的亚群。我们还表明，PHATE 适用于多种数据类型，包括质谱流式细胞术、单细胞 RNA 测序、Hi-C 和肠道微生物组数据。

更新日期：2019-12-04

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